System and method for overlap detection in surveillance camera network

ABSTRACT

A system and method for overlap detection in a surveillance camera network is disclosed. An analytics system of the system receives image data captured from the cameras and detects motion within the image data. The analytics system then correlates detected motion among the image data from the different cameras and determines overlap of fields of view of the cameras. In a preferred embodiment, a mobile user device held by an installer is moved through a critical path at the premises while the cameras are capturing the image data, and the analytics system determines whether the user device/installer is included in the image data from at least two of the cameras. In another embodiment, the analytics system determines overlap by determining correlated motion detection events associated with motion occurring at substantially the same time in image data from two or more different surveillance cameras and inferring that the motion is related.

RELATED APPLICATIONS

This application is related to:

U.S. application Ser. No. 15/076,701 filed on Mar. 22, 2016, entitled“Method and system for surveillance camera arbitration of uplinkconsumption,” now U.S. Patent Publication No.: 2017/0278368 A1;

U.S. application Ser. No. 15/076,703 filed on Mar. 22, 2016, entitled“Method and system for pooled local storage by surveillance cameras,”now U.S. Patent Publication No.: 2017/0280102 A1;

U.S. application Ser. No. 15/076,704 filed on Mar. 22, 2016, entitled“System and method for designating surveillance camera regions ofinterest,” now U.S. Patent Publication No.: 2017/0277967 A1;

U.S. application Ser. No. 15/076,705 filed on Mar. 22, 2016, entitled“System and method for deadzone detection in surveillance cameranetwork,” now U.S. Patent Publication No.: 2017/0278366 A1;

U.S. application Ser. No. 15/076,708 filed on Mar. 22, 2016, entitled“System and method for retail customer tracking in surveillance cameranetwork,” now U.S. Patent Publication No.: 2017/0278137 A1;

U.S. application Ser. No. 15/076,709 filed on Mar. 22, 2016, entitled“Method and system for modeling image of interest to users,” now U.S.Patent Publication No.: 2017/0277785 A1;

U.S. application Ser. No. 15/076,710 filed on Mar. 22, 2016, entitled“System and method for using mobile device of zone and correlated motiondetection,” now U.S. Patent Publication No.: 2017/0280103 A1;

U.S. application Ser. No. 15/076,712 filed on Mar. 22, 2016, entitled“Method and system for conveying data from monitored scene viasurveillance cameras,” now U.S. Pat. No. 9,965,680;

U.S. application Ser. No. 15/076,713 filed on Mar. 22, 2016, entitled“System and method for configuring surveillance cameras using mobilecomputing devices,” now U.S. Patent Publication No.: 2017/0278365 A1;

and

U.S. application Ser. No. 15/076,717 filed on Mar. 22, 2016, entitled“System and method for controlling surveillance cameras,” now U.S.Patent Publication No.: 2017/0280043 A1.

All of the afore-mentioned applications are incorporated herein by thisreference in their entirety.

BACKGROUND OF THE INVENTION

Surveillance camera systems are often deployed to collect image datawithin or around premises. Examples of premises include governmentalbuildings, office buildings, retail establishments, and single andmulti-unit residences. The cameras are typically installed to monitorand detect individuals and/or activities at different locations in andaround the premises.

A successful installation of surveillance camera systems requirescareful consideration of several factors. The designers/installersselect the locations in which to install the cameras, select the type ofcamera that is best suited for each location, and then position thecameras' fields of view to capture scenes at each location. For example,point of sale areas might require one or more ceiling mounted, domestyle cameras to capture transaction-related activities within thelocations. For monitoring large open areas such as shopping malls,open-floor plan offices, and parking lots, either panoramic view (e.g.“fish eye”) cameras or pan-tilt-zoom (PTZ) cameras are often utilizedbecause of each camera's ability to provide wider fields of view and toscan the areas, respectively. Designers/installers might also positionthe fields of view of different surveillance cameras to overlap, andalso position the field of view of one camera to include anothersurveillance camera. These actions provide different views orperspectives of the same scene and the ability to capture attempts attampering with the surveillance cameras.

Analytics systems are often part of surveillance camera systems. At abasic level, the analytics systems provide the ability to detect andtrack individuals and objects within the image data of the monitoredscenes. Other capabilities include the ability to determine motion ofobjects relative to visual cues superimposed upon the image data and tosearch for specific behaviors of interest within the image data. Thevisual cues are often placed near fixed objects in the background sceneof the image data to infer motion of objects relative to the visualcues. In one example, virtual tripwire visual cues can be located nearentryways within the scene to detect entry or exit of individualsthrough the entryways and to provide a count of the individuals passingthrough the entryway over a specific time period. These analyticssystems can provide both real-time analysis of live image data andforensic analysis of previously recorded image data.

SUMMARY OF THE INVENTION

It would be beneficial to determine overlap among fields of view ofsurveillance cameras during the installation of the surveillance camerasusing an analytics system, for example. In contrast, installers ofcurrent surveillance camera systems might typically use an “educatedguess” approach for installing surveillance cameras to provide thedesired level of overlap among the fields of view, where the experienceof the installer is paramount to achieving this objective.

It would also be beneficial to infer overlap among fields of view ofsurveillance cameras from image data captured by the surveillancecameras. Such a capability could allow system operators to betterinterpret image data from different cameras. Moreover, the analyticssystems could use this information to present image data to operators ina way that is easier to grasp context.

The present invention provides for the ability to analyze image datafrom multiple surveillance cameras. It does this analysis using a mobilecomputing device such as a smartphone or tablet computing device or evena laptop computer. These modern devices have excellent image dataprocessing resources and can be used to tap the image data feeds fromnearby surveillance cameras and analyze that image data to provideinformation on the configuration of the system as a whole.

In general, according to one aspect, the invention features a method fordetermining overlap for a network of surveillance cameras. The methodcomprises detecting motion within image data from the network ofsurveillance cameras, correlating detected motion among the image datafrom different surveillance cameras, and determining overlap of fieldsof view of the surveillance cameras.

In embodiments, motion is detected within image data from the network ofsurveillance cameras by tracking a mobile user device and/or aninstaller carrying the mobile user device as the mobile user device ismoved along a critical path within a premises.

For example, a mobile user device might be used for traversing acritical path within a premises and the correlated detected motiondetected within the image data to determine whether the mobile userdevice and/or the installer is included in the image data from at leasttwo of the surveillance cameras.

Objects can be identified within the image data and the objects trackedacross the fields of view of the surveillance cameras. Motion detectionevents can be generated in response to detecting motion within the imagedata, and correlating the motion detection events.

In some embodiments, a display grid is provided, which includes imagedata from at least two surveillance cameras having at least one portionof an object within a scene monitored by the surveillance camerasincluded within the image data, and sending the display grid for displayon a mobile user device.

In general, according to another aspect, the invention features a methodfor determining overlap of fields of view for surveillance cameras of anetwork. The method comprises defining a path within monitored by thesurveillance cameras via a mobile user device carried by the installer,the surveillance cameras capturing image data of the scene during thedefinition of the path and transferring the image data to an analyticssystem, and the analytics system determining overlap from the imagedata.

In general, according to another aspect, the invention features a methodfor determining overlap of fields of view for surveillance cameras. Thismethod comprises receiving time-stamped image data from two or moresurveillance cameras, analyzing the image data to determine correlatedmotion, and determining overlap of the fields of view based on thecorrelated motion.

In general, according to still another aspect, the invention features asurveillance camera system, which comprises two or more surveillancecameras generating image data of a scene and an analytics system thatreceives the image data from the two or more surveillance cameras anddetermines overlap within the scene by determining whether the imagedata from at least two of the surveillance cameras includes correlatedmotion.

In general, according to still another aspect, the invention features asurveillance camera system, comprising two or more surveillance camerasgenerating image data of a scene and an analytics system that receivesthe image data from the surveillance cameras and determines overlapwithin the scene by correlating detected motion among the image datafrom different surveillance cameras, and by determining that thecorrelated detected motion occurs at substantially the same time in theimage data from two or more different surveillance cameras and inferringthat the motion is related.

In general, according to still another aspect, the invention features asurveillance camera system. The system comprises a mobile user devicefor defining a critical path, surveillance cameras capturing image datacapturing image data along the critical path and an analytics systemdetermining overlap of fields of view of the surveillance cameras fromthe image data.

In general, according to still another aspect, the invention featuressurveillance camera system. This system comprises surveillance camerasfor generating time-stamped image data and an analytics system fordetermine correlated motion in the image data and determining overlap offields of view of the surveillance cameras based on the correlatedmotion.

The above and other features of the invention including various noveldetails of construction and combinations of parts, and other advantages,will now be more particularly described with reference to theaccompanying drawings and pointed out in the claims. It will beunderstood that the particular method and device embodying the inventionare shown by way of illustration and not as a limitation of theinvention. The principles and features of this invention may be employedin various and numerous embodiments without departing from the scope ofthe invention.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings, reference characters refer to the sameparts throughout the different views. The drawings are not necessarilyto scale; emphasis has instead been placed upon illustrating theprinciples of the invention. Of the drawings:

FIG. 1 is a schematic diagram showing a network of surveillance camerasinstalled at a premises, where each of the surveillance cameras ispositioned to monitor an area at a premises for illustrating differentembodiments of the invention for determining whether overlap exists inthe scenes monitored by the surveillance cameras;

FIG. 2A-2C are exemplary frames of image data representative of thefields of view of each of the surveillance cameras labeled camera1,camera2, and camera3 in FIG. 1, respectively, and where the image datais associated with objects moving in a monitored alley adjacent to apremises;

FIG. 3 is a schematic diagram showing some of the components of anexemplary surveillance camera;

FIG. 4 is a sequence diagram showing a preferred installer methodembodiment of the present invention, where the method enables aninstaller to determine overlap among fields of view of the surveillancecameras during installation of the cameras, and where the fields of viewinclude an installer carrying a mobile user device as the installer/userdevice moves along a critical path within a monitored corridor of thepremises;

FIG. 5 is a flow chart showing a method for another embodiment of theinvention, where an analytics system infers overlap from image datacaptured by the surveillance cameras;

FIG. 6 shows a floor plan map of an area within a premises monitored bysurveillance cameras, where the methods of FIG. 4 and FIG. 5 determineoverlap in the area monitored by the surveillance cameras, include theoverlap as shaded areas within the map, and present the map for displayon a mobile user device; and

FIG. 7 shows an exemplary intelligent display grid created by theanalytics system in the method of FIG. 5 that includes overlappingframes of image data, where the image data is representative of thefields of view of the cameras in FIG. 2A-2C, and where the intelligentdisplay grid is presented for display on the mobile user device forenabling an operator to obtain an “at a glance” determination of overlapamong the fields of view of the surveillance cameras.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The invention now will be described more fully hereinafter withreference to the accompanying drawings, in which illustrativeembodiments of the invention are shown. This invention may, however, beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the invention to those skilled in the art.

As used herein, the term “and/or” includes any and all combinations ofone or more of the associated listed items. Further, the singular formsincluding the articles “a”, “an” and “the” are intended to include theplural forms as well, unless expressly stated otherwise. It will befurther understood that the terms: includes, comprises, including and/orcomprising, when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof. Further, it will be understood that when anelement, including component or subsystem, is referred to and/or shownas being connected or coupled to another element, it can be directlyconnected or coupled to the other element or intervening elements may bepresent.

FIG. 1 shows an exemplary surveillance camera system 10 to which theinvention is applicable.

Surveillance cameras 103-1 through 103-3, labeled camera1 throughcamera3, have fields of view 105-1 through 105-3, respectively. Thecameras 103 are installed to monitor a corridor 70 within a premises 52,in one example. In the illustrated example, a doorway 66 is located atthe end of the corridor 70. In another example, the cameras 103 areinstalled around a premises to monitor an alley 71 adjacent to thepremises 52.

The surveillance cameras 103 communicate with each other over a localnetwork 210 and with a local analytics system 222. The analytics system222 creates an intelligent display grid 418 that includes image data 250from at least two surveillance cameras 250. The system 10 might alsoinclude a network video recorder 228 that stores image data 250 capturedby the surveillance cameras 103.

In other examples, the analytics system 222 could be a cloud basedsystem that is accessible through a public network. In still otherexamples, the analytics system, as illustrated below, is integrated onone or more of the cameras or distributed over the processing resourcesof the several cameras and/or standalone systems. Such an analyticssystem could be partially or completely executing on the surveillancecameras 103.

However implemented, the analytics system 222 preferably includes orutilizes a map 180, which is an image representation of the area of thepremises 52 (e.g. the corridor 70 and/or alley 71) under surveillance bythe cameras 103. The installer 60 typically loads the map 180 onto theanalytics system 222 after initial installation of the cameras 103 butprior to analyzing the corridor 70 and/or alley 71 for instances ofoverlap 90. The map 180 further preferably includes the locations ofeach of the cameras 103-1 through 103-3 of the network.

Each of the surveillance cameras 103-1 through 103-3 capture image data250 of a scene within their respective fields of view 105-1 through105-3 of the cameras. The surveillance cameras 103 transmit their imagedata 250 over the local network 210 for analysis by the analytics system222 or retain the image data 250 to determine instances of overlap 90between the scenes/fields of view 105 of the cameras 103.

A wireless router 244 provides a wireless network 230 such as WiFi or acellular wide area network that enables exchange of wireless messages264 between components. The wireless router 244 also has a local networkinterface that connects the wireless router 244 to the local network210.

In one implementation, an installer 60-1 holds a user mobile computingdevice 400, also known as user device, for communicating with thesurveillance cameras 103. Examples of user devices 400 includesmartphones, tablet computing devices, and laptop computers runningoperating systems such as Windows, Android, Linux, or IOS, in examples.Each user device 400 includes a display screen or touch screen 410 andone or more applications 412, or “apps.” The apps 412 execute upon theoperating systems of the user devices 400.

The user device 400 can exchange wireless messages 264 directly betweeneach surveillance camera 103 and/or the analytics systems 222 for thispurpose. Exemplary wireless messages 264-1, 264-2 and 264-5 between userdevice 400 and surveillance cameras 103-1, 103-2, and 103-3 are shown.The surveillance cameras 103 also transmit their image data 250 over thewireless network 230 to the user device 400 in the wireless messages 264via the wireless router 244 or directly via peer-to-peer connections.Even Bluetooth or similar protocol could be used. The user device 400receives the wireless messages 264, extracts the image data 250 therein,and forwards the image data 250 to an analytics system to determineinstances of overlap 90 between the scenes/fields of view 105 of thecameras 103.

It is important to note that additional embodiments of the analyticssystem 222 can exist in the system 10. In other examples, the analyticssystem 222 can be a remote analytics system maintained by a third partyentity and which the surveillance cameras 103 access over a networkcloud, a process running on the user devices 400, or a processintegrated within one or more of the surveillance cameras 103. For thelatter example, one of the cameras 103 typically functions as a masterin a master/slave relationship between the cameras 103. The remainingcameras 103 functioning as slaves in this relationship transmit theirimage data 250 over the network 210 for analysis by the integratedanalytics system of the master surveillance camera 103.

Via the wireless messages 264, user device 400 sends instructions toconfigure the cameras 103 and access the image data 250 on the cameras103. The wireless messages 264 include both control and data wirelessmessages. In one example, data wireless messages 264 include frames ofimage data 250 that the surveillance cameras 103 send to the user mobilecomputing devices 400.

Specific examples showing how the cameras 103 might be deployed areillustrated. In one example, dome style cameras camera2 and camera3 aremounted overhead within a premises 52 to monitor corridor 70. Camera1 isa PTZ style camera mounted along a wall of corridor 70 such that thefield of view 105-1 of camera1 provides a side view of the corridor 70.In another example, similar dome style cameras camera2 and camera3 aremounted overhead outside the premises 52 to monitor alley 71. In thisexample, camera1 might also be a PTZ style camera mounted along a wallof an adjacent building such that the field of view 105-1 of camera1provides a side view of the alley 71.

An installer 60-1 might initially position camera1 and camera2 such thattheir fields of view 105-1 and 105-2 include a common portion of thescene, indicated by overlap 90-1. In a similar fashion, the installer60-1 positions camera2 and camera3 to include a different portion of thescene in common between the fields of view 105-2 and 105-3 of camera2and camera3, indicated by overlap 90-2. However, the initial positioningof the cameras 103 to achieve the desired overlap 90-1/90-2 or nooverlap is based on an educated guess and requires verification. Todetermine that the desired amount of overlap 90-1/90-2 is achieved, inembodiments, the installer 60-1 utilizes the user device 400 inconjunction with the cameras 103 and the analytics system 222.

In a preferred embodiment, with respect to the corridor 70 monitoredarea example, the system 10 enables determination of overlap 90-1/90-2during the installation of the cameras 103 in response to the installer60-1 walking a critical path 54 through the monitored scene (e.g.corridor 70) while carrying the user device 400. The cameras 103 capturethe installer/user device in the image data 250 of each of the cameras103 during the traversal of the critical path 54, and send the imagedata 250 to the analytics system 222 to determine the overlap 90-1/90-2based on correlating detected motion of the installer/user device amongoverlapping frames of image data 250.

In another embodiment, with respect to the alley 71 monitored areaexample, the analytics system 222 of the system 10 determines overlap90-1/90-2 within the scene by first determining motion of objects inimage data 250 of the scene. Unlike the preferred embodiment, where themotion is associated with a known and predetermined object moving withinthe scene in a specific manner (e.g. the installer/user device movingthrough the scene along the critical path 54), the objects and theirexpected manner of movement are not predetermined. In the example,objects such as a dog 88 and individual 60-2 are moving through thealley 71. Then, the analytics system 222 correlates the detected motionamong the image data 250 from the surveillance cameras 103, determinesthat the correlated detected motion occurs at substantially the sametime in the image data 250 from two or more different surveillancecameras 103, and infers that the motion is related and thus that thecameras have overlapping fields of view and the degree of that overlap.

It is also important to note that the analysis of the image data 250provided by the analytics system 222 can either be executed in realtime, or at a time after the cameras 103 are installed, in a forensicsfashion. For the real time analysis, the analytics system 222 preferablyreceives the image data 250 over the local network 210 from the cameras103 just after the cameras 103 capture the image data 250 of the scene.For the forensic analysis of the image data 250, the analytics system222 can analyze previously recorded image data 250 of the scene storedon a network video recorder 228, or image data 250 stored locally withinthe cameras 103, in examples.

FIG. 2A-2C show exemplary frames of image data 250-1 through 250-3 fromcamera1, camera2, and camera3, respectively. With respect to FIG. 1, theobjects included within the image data 250 are associated with the alley71 monitored area and are shown to illustrate overlap among image data250/fields of view 105 of the cameras 103.

In FIG. 2A, a frame of image data 250-1 from camera1 includes a scene ofthe alley 71 including doorway 66 and individual 60-2. In FIG. 2B, aframe of image data 250-2 from camera2 includes a different scene of thesame alley 71, where the scene includes individual 60-2 and a portion ofdog 88. Finally, in FIG. 2C, a frame of image data 250-3 from camera2includes yet another scene of the same alley 71, where the sceneincludes the entirety of the dog 88. With respect to FIG. 2A and FIG.2B, because at least a portion of the same object (here, the entirety ofindividual 60-2) exists in at least two fields of view 105-1/105-2, theimage data 250-1/250-2 is said to overlap. With respect to FIG. 2A andFIG. 2B, because at least a portion of the same object (here, the fronthalf of dog 88) exists in at least two fields of view 105-2/105-3, theimage data 250-2/250-3 is said to overlap.

FIG. 3 shows some of the components of an exemplary surveillance camera103.

The camera 103 includes a processing unit (CPU) 138, an imager 140, acamera image data storage system 174 and a network interface 142. Anoperating system 136 runs on top of the CPU 138. A number of processesor applications are executed by the operating system 136. One of theprocesses is a control process 162. In some embodiments, a cameraanalytics system 176 process is also included within one or more of thesurveillance cameras 103 in the network 210. This camera analyticssystem 176 can also create an intelligent display grid 418.

The camera 103 saves image data 250 captured by the imager 140 to thecamera image data storage system 174, locally, and/or to the NVR 228,remotely. Each camera 103 can support one or more streams of image data250. The control process 162 receives and sends messages 264 via itsnetwork interface 142. Each camera 103 also saves metadata 160 for theimage data 250, including a timestamp 164 and camera number 166 for eachframe of image data 250.

FIG. 4 describes a preferred “installer method” embodiment of the system10 for determining overlap 90 among the fields of view of thesurveillance cameras 103. The method is described by way of an examplecritical path 54 traversal by an installer carrying a user device 400,where the installer/user device moves through a monitored area (e.g. thecorridor 70 of FIG. 1) along the critical path 54. The method alsoprovides details of interactions between major components of the system10 both during and after this process.

In step 502, an app 412 running on the user device 400 sends a pairingrequest to one or more cameras 103 to establish a communications session308 with each of the cameras 103 and for the surveillance cameras 103 toenter an overlap detection mode. According to step 504, the cameras 103send a pairing response message and enter overlap detection mode. As aresult of step 504, a communication session 308 is established betweenthe each of the cameras 103 currently in overlap detection mode and theapp 412, in one example. In other examples, the communication isestablished with the analytics systems 222, 176.

In step 506, the app 412 then presents user interface buttons to startand stop definition of a critical path 54 within an area being monitored(e.g. corridor 70) by multiple surveillance cameras 103.

According to step 508, in response to selection of a “Start” button onthe user interface of the app 412 by the installer 60, the app 412starts a local timer and sends an instruction to the cameras 103 toindicate start of the critical path definition. In step 510, theinstaller/user device moves through the area being monitored to define acritical path 54 through the area being monitored.

In step 514, in response to the receiving the “start” buttoninstruction, at regular time intervals, each of the surveillance cameras103 sends time-stamped image data 250 captured during the definition ofthe critical path 54 to an analytics system 222. According to step 516,in response to selection of a “Stop” button on the user interface of theapp 412 by the installer 60, the app 412 stops the local timer and sendsan instruction to the cameras 103 to end definition of the critical path54. In response to receiving the “stop” instruction, the cameras 103send their remaining image data 250 to the analytics system 222, in step518.

According to step 520, the analytics system 222 and/or 176 receives thetime stamped image data 250 from the surveillance cameras 103 duringdefinition of the critical path 54. In step 522, the app 412 sends thetime interval over which to analyze the image data 250, indicated by thevalue of the local timer. The analytics system 222 and/or 176 thentracks the installer/user device through the image data 250 from thesurveillance cameras 103, in step 524.

In step 526, the analytics system 222 and/or 176 determines overlap 90among fields of view 105 of each the cameras 103 by correlating themotion detection events, and determining from the correlated detectedmotion whether the installer/user device is included within the fieldsof view 105 of at least two or more different fields of view 105 of thecameras 103 at substantially the same time. Then, in step 528, theanalytics system 222 and/or 176 includes image data 250 associated withthe determined overlap 90 (e.g. overlapping fields of view 105 of thecameras 103) within an intelligent display grid 418 and sends theintelligent display grid 418 for display on the user device 400. Theimage data 250 displayed within the display grid 418 is from at leasttwo surveillance cameras 103 and has at least one portion of an objectwithin a scene monitored by the surveillance cameras included within theimage data 250.

According to step 530, the app 412 displays the intelligent display grid418 on the display screen 410 of the user device 400, and the installer60-1 uses the displayed image data 250 within the intelligent displaygrid 418 concerning overlap 90 between fields of view 105 for each ofthe surveillance cameras 103 to determine whether the cameras 103require repositioning to achieve the desired amount of overlap 90.

In step 532, the installer 60-1 optionally repeats this process toeither verify that repositioning of the cameras 103 and/or changingsettings of the cameras 103 (e.g. lens, zoom) achieves the desiredoverlap 90 or to define additional critical path(s) 54 and detectoverlap 90 therein. Changing the lenses of the cameras 103 can cause acorresponding change in the fields of view 105 of the cameras 103 forachieving the desired overlap 90. This change is required, in oneexample, when the lenses are of a fixed focus type, which are designedto work for a single, specific working distance. Replacing a fixed lenswith a varifocal lens, in one example, enables the installer 60-1 tosubsequently adjust the focal length, angle of view, and level of zoomof the cameras 103, thereby enabling adjustment of overlap 90 amongfields of view 105 of two or more surveillance cameras 103.Additionally, changing a zoom setting of the cameras 103 can cause acorresponding change in the fields of view 105 of the cameras 103 inaccordance with the installer's overlap 90 objectives. This is a typicalcourse of action for adjusting overlap 90 when the cameras 103 are PTZtype cameras, in one example. In step 534, the installer 60-1 selects anoption within the app 412 to exit overlap detection mode, and the app412 sends an associated message 264 to the cameras 103 in response.Finally, in step 536, the cameras 103 receive the exit message 264, andend overlap detection mode and terminate the communications session 308in response.

FIG. 5 describes a method for another embodiment, where the overlap 90is inferred from image data 250 taken of the scene and sent by theanalytics system 222 and/or 176.

In step 550, the analytics system 222 and/or 176 receives time-stampedimage data 250 from two or more surveillance cameras 103 at the samepremises 52, where fields of view 105 of the two or more of thesurveillance cameras 103 are positioned to overlap a monitored areawithin the premises 52.

In step 552, the analytics system 222 and/or 176 analyzes the image data250 to determine correlated motion detection events, where eachcorrelated motion detection event is associated with motion occurring atsubstantially the same time in image data 250 from two or more differentsurveillance cameras 103 and inferring that the motion is related.

Then, in step 554, from the determined correlated motion detectionevents, the analytics system 222 builds an intelligent display grid 418including the overlapping frames of image data 250 and sends theintelligent display grid 418 for display on the user device 400, therebyproviding a visual indication of the degree of overlap 90 for theinstaller or operator 60-1. Upon conclusion of step 554, the methodtransitions back to step 550 to receive the next frames of time-stampedimage data 250 from the surveillance cameras 103.

FIG. 6 shows an image representation of a map 180 of a monitored arearendered on a display screen 410 of a user device 400. The map 180 isfor monitored area (e.g. corridor 70 and/or alley 71) of the premises 52in FIG. 1. The map 180 has been modified by the overlap detectionmethods of FIG. 3 and FIG. 4. The analytics systems of these methodswere able to determine overlap regions 90-1 and 90-2, and highlightedthe overlap 90 graphically within the map 180 as shaded areas beforesending the map 180 for display on the user device 400. This enables theinstaller 60 or operator to have an “at a glance” visual indication ofoverlap 90 among the fields of view 105 of two or more surveillancecameras 103.

FIG. 7 shows an image representation of an intelligent display grid 418rendered on a display screen 410 of a user device 400 in accordance withthe method of FIG. 5. The intelligent display grid 418 enables theinstaller or operator 60 to have an “at a glance” visual indication ofinstances of overlap 90-1 and 90-2 within the image data 250 andtherefore an indication of overlap 90-1/90-2 associated with fields ofview 105 of the cameras 103.

The intelligent display grid 418 includes panes 289. Each of the panes289 can include image data 250, where the image data 250 is provided bythe analytics system 222 and/or 176 in accordance with the overlapdetection methods of FIG. 4 and FIG. 5. The image data 250 displayed inthe panes 289 are formatted by the analytics system 222 and/or 176 intoframes to enable a visual representation of the image data 250 withinthe panes 289. Metadata 160-1 through 160-3 for each of the frames ofimage data 250-1 through 250-3 are optionally displayed within panes289-1 through 289-3, respectfully, of the intelligent display grid 418.Pane 289-4 is included within the display grid 418 to provide asymmetric visual presentation of the grid 418 but does not include anyimage data 250.

With reference to the method of FIG. 5 and image data 250-2 and 250-1,in one example, the analytics system 222 and/or 176 infers overlap 90-1among frames of image data 250-2/250-1 by first detecting motion in theimage data 250-2/250-1 associated with individual 60-2, and generatingmotion detection events for the detected motion. Then, the analyticssystem 222 correlates the motion detection events to determine if theevents occurred at substantially the same time. Here, because the valueof timestamp 164-2 for image data 250-2 of camera2 is only one secondgreater than the timestamp 164-1 for image data 250-1 of camera1, theanalytics system 222 can infer that the correlated motion detectionevents are likely related. As a result, the analytics system 222includes the image data 250-1 and 250-2 in the intelligent display grid418 to provide a visual indication of the overlap 90-1 between the imagedata 250-1/250-2, and therefore to provide a visual indication ofoverlap 90-1 among associated fields of view 105-1/105-2 of camera1103-1 and camera2 103-2, respectively.

With reference to the method of FIG. 5 and image data 250-3 and 250-2,in another example, the analytics system 222 and/or 176 infers overlap90-2 among frames of image data 250-3/250-2 by first detecting motion inthe image data 250-3/250-2 associated with individual 60-2 and/or dog88, and generating motion detection events for the detected motion.Then, the analytics system 222 correlates the motion detection events todetermine if the events occurred at substantially the same time. Here,because the value of timestamp 164-3 for image data 250-3 of camera3 isonly one second greater than the timestamp 164-2 for image data 250-2 ofcamera2, the analytics system 222 can infer that the correlated motiondetection events are likely related. As a result, the analytics system222 includes the image data 250-3 and 250-2 in the intelligent displaygrid 418 to provide a visual indication of the overlap 90-2 between theimage data 250-2/250-3, and therefore to provide a visual indication ofoverlap 90-2 among associated fields of view 105-2/105-3 of camera2103-2 and camera3 103-3, respectively.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

What is claimed is:
 1. A method for determining overlap of fields ofview for surveillance cameras of a network, comprising: defining acritical path monitored by the surveillance cameras via a mobile userdevice carried by the installer that presents a user interface to startand stop definition of the critical path; the surveillance camerascapturing image data of the scene during the definition of the criticalpath and transferring the image data to an analytics system; and theanalytics system determining overlap from the image data along thecritical path.
 2. The method of claim 1, wherein the analytics systemdetermining the overlap from the image data comprises the analyticssystem correlating motion within the image data to determine overlap forat least two of the surveillance cameras.
 3. The method of claim 2,wherein detecting the motion within the image data from the network ofsurveillance cameras comprises tracking the mobile user device and/or aninstaller carrying the mobile user device as the mobile user device ismoved along the path within a premises.
 4. The method of claim 2,further comprising: the mobile user device traversing the path within apremises; and the analytics system determining from correlated detectedmotion within the image data from the surveillance cameras whether themobile user device and/or the installer is included in the image datafrom at least two of the surveillance cameras.
 5. The method of claim 4,further comprising determining that the image data from the at least twoof the surveillance cameras was captured at substantially the same time.6. The method of claim 4, wherein detecting motion within image datafrom the network of surveillance cameras comprises identifying objectswithin the image data and tracking the objects across the fields of viewof the surveillance cameras.
 7. The method of claim 4, whereincorrelating detected motion among the image data from differentsurveillance cameras comprises generating motion detection events inresponse to detecting motion within the image data, and correlating themotion detection events.
 8. The method of claim 1, further comprisingbuilding a display grid including image data from at least twosurveillance cameras having at least one portion of an object within ascene monitored by the surveillance cameras included within the imagedata, and sending the display grid for display on a user device.
 9. Asurveillance camera system, comprising: two or more surveillance camerasgenerating image data of a scene; a user device that presents a userinterface to start and stop definition of a critical path; and ananalytics system that: receives the image data from the surveillancecameras; tracks the mobile user device and/or an installer carrying themobile user device as the mobile user device is moved along the criticalpath within a premises; and determines overlap within the scene bycorrelating detected motion among the image data from differentsurveillance cameras, and by determining that the correlated detectedmotion occurs at substantially the same time in the image data from twoor more different surveillance cameras and inferring that the motion isrelated.
 10. The method of claim 9, further comprising building adisplay grid that includes the image data associated with the correlatedmotion and displaying the display grid to provide a visual indication ofthe overlap.
 11. The system of claim 9, wherein the analytics systemcreates a display grid that includes image data from at least twosurveillance cameras.
 12. The system of claim 9, wherein the analyticssystem tracks a mobile user device and/or an installer carrying themobile user device as the mobile user device is moved along a criticalpath within a premises.
 13. The system of claim 9, wherein the analyticssystem determines from the correlated motion within the image datawhether the mobile user device and/or the installer is included in theimage data from at least two of the surveillance cameras.
 14. The systemof claim 13, wherein the analytics system determines that the image datafrom the at least two of the surveillance cameras was captured atsubstantially the same time.
 15. The system of claim 9, wherein theanalytics system identifies objects within the image data and tracks theobjects across fields of view of the surveillance cameras.
 16. Thesystem of claim 9, wherein the analytics system generates motiondetection events in response to detecting motion within the image data,and correlates the motion detection events.